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Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development

Barber, Paul R; Mustapha, Rami; Flores-Borja, Fabian; Alfano, Giovanna; Ng, Kenrick; Weitsman, Gregory; Dolcetti, Luigi; ... Ng, Tony; + view all (2022) Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development. eLife , 11 , Article e73288. 10.7554/eLife.73288. Green open access

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Abstract

BACKGROUND: Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model. METHODS: Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS. RESULTS: A 'baseline' and a 'combined' risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome. CONCLUSIONS: This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course. FUNDING: Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy's and St Thomas' NHS Foundation Trust and The Institute of Cancer Research. CLINICAL TRIAL NUMBER: NCT02633800.

Type: Article
Title: Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.7554/eLife.73288
Publisher version: https://doi.org/10.7554/eLife.73288
Language: English
Additional information: © 2022, Barber, Mustapha, Flores-Borja et al. This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10162955
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